Bounds on Lyapunov Exponents via Entropy Accumulation
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Date
2021-01-01
Publication Type
Journal Article
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Abstract
Lyapunov exponents describe the asymptotic behavior of the singular values of large products of random matrices. A direct computation of these exponents is however often infeasible. By establishing a link between Lyapunov exponents and an information theoretic tool called entropy accumulation theorem we derive an upper and a lower bound for the maximal and minimal Lyapunov exponent, respectively. The bounds assume independence of the random matrices, are analytical, and are tight in the commutative case as well as in other scenarios. They can be expressed in terms of an optimization problem that only involves single matrices rather than large products. The upper bound for the maximal Lyapunov exponent can be evaluated efficiently via the theory of convex optimization.
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published
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Journal / series
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67 (1)
Pages / Article No.
10 - 24
Publisher
IEEE
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Subject
Lyapunov exponent; random matrices; quantum entropy; convex optimization
Organisational unit
03781 - Renner, Renato / Renner, Renato
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Funding
165843 - Fully quantum thermodynamics of finite-size systems (SNF)
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